using Microsoft.Extensions.Logging;
using SmartMedicalRAG.Core.Models;

namespace SmartMedicalRAG.Core.Services;

/// <summary>
/// 多模态数据服务实现
/// </summary>
public class MultimodalDataService : IMultimodalDataService
{
    private readonly ILogger<MultimodalDataService> _logger;
    private readonly ITextProcessor _textProcessor;
    private readonly IVoiceProcessor _voiceProcessor;
    private readonly IImageProcessor _imageProcessor;
    private readonly IPhysiologicalSignalProcessor _signalProcessor;

    public MultimodalDataService(
        ILogger<MultimodalDataService> logger,
        ITextProcessor textProcessor,
        IVoiceProcessor voiceProcessor,
        IImageProcessor imageProcessor,
        IPhysiologicalSignalProcessor signalProcessor)
    {
        _logger = logger;
        _textProcessor = textProcessor;
        _voiceProcessor = voiceProcessor;
        _imageProcessor = imageProcessor;
        _signalProcessor = signalProcessor;
    }

    public async Task<ProcessedData> ProcessTextAsync(string text)
    {
        try
        {
            _logger.LogInformation("开始处理文本数据: {TextLength} 字符", text.Length);
            
            // 医学术语标准化
            var standardizedText = await _textProcessor.StandardizeMedicalTermsAsync(text);
            
            // 实体识别
            var entities = await _textProcessor.ExtractEntitiesAsync(standardizedText);
            
            var result = new ProcessedData
            {
                Type = DataType.Text,
                Content = standardizedText,
                Entities = entities,
                Confidence = 0.98f
            };
            
            _logger.LogInformation("文本数据处理完成，识别到 {EntityCount} 个实体", entities.Count);
            return result;
        }
        catch (Exception ex)
        {
            _logger.LogError(ex, "文本数据处理失败");
            throw;
        }
    }

    public async Task<ProcessedData> ProcessVoiceAsync(byte[] audioData)
    {
        try
        {
            _logger.LogInformation("开始处理语音数据: {AudioSize} 字节", audioData.Length);
            
            // 语音转文字 (方言支持)
            var transcription = await _voiceProcessor.TranscribeAsync(audioData);
            
            // 语义理解
            var semanticResult = await _voiceProcessor.UnderstandSemanticsAsync(transcription);
            
            var result = new ProcessedData
            {
                Type = DataType.Voice,
                Content = transcription,
                SemanticUnderstanding = semanticResult,
                Confidence = 0.97f
            };
            
            _logger.LogInformation("语音数据处理完成，转写结果: {Transcription}", transcription);
            return result;
        }
        catch (Exception ex)
        {
            _logger.LogError(ex, "语音数据处理失败");
            throw;
        }
    }

    public async Task<ProcessedData> ProcessImageAsync(byte[] imageData)
    {
        try
        {
            _logger.LogInformation("开始处理图像数据: {ImageSize} 字节", imageData.Length);
            
            // 医学图像分割
            var segmentation = await _imageProcessor.SegmentLesionsAsync(imageData);
            
            // 特征提取
            var features = await _imageProcessor.ExtractFeaturesAsync(imageData);
            
            var result = new ProcessedData
            {
                Type = DataType.Image,
                Segmentation = segmentation,
                Features = features,
                Confidence = 0.92f
            };
            
            _logger.LogInformation("图像数据处理完成");
            return result;
        }
        catch (Exception ex)
        {
            _logger.LogError(ex, "图像数据处理失败");
            throw;
        }
    }

    public async Task<ProcessedData> ProcessPhysiologicalSignalAsync(PhysiologicalData data)
    {
        try
        {
            _logger.LogInformation("开始处理生理信号数据: {DataType}, {ValueCount} 个数据点", data.Type, data.Values.Count);
            
            // 异常模式识别
            var anomalies = await _signalProcessor.DetectAnomaliesAsync(data);
            
            // 时间序列分析
            var timeSeriesAnalysis = await _signalProcessor.AnalyzeTimeSeriesAsync(data);
            
            var result = new ProcessedData
            {
                Type = DataType.PhysiologicalSignal,
                Anomalies = anomalies,
                TimeSeriesAnalysis = timeSeriesAnalysis,
                Confidence = 0.95f
            };
            
            _logger.LogInformation("生理信号数据处理完成");
            return result;
        }
        catch (Exception ex)
        {
            _logger.LogError(ex, "生理信号数据处理失败");
            throw;
        }
    }
}

/// <summary>
/// 文本处理器接口
/// </summary>
public interface ITextProcessor
{
    Task<string> StandardizeMedicalTermsAsync(string text);
    Task<List<string>> ExtractEntitiesAsync(string text);
}

/// <summary>
/// 语音处理器接口
/// </summary>
public interface IVoiceProcessor
{
    Task<string> TranscribeAsync(byte[] audioData);
    Task<object> UnderstandSemanticsAsync(string transcription);
}

/// <summary>
/// 图像处理器接口
/// </summary>
public interface IImageProcessor
{
    Task<object> SegmentLesionsAsync(byte[] imageData);
    Task<object> ExtractFeaturesAsync(byte[] imageData);
}

/// <summary>
/// 生理信号处理器接口
/// </summary>
public interface IPhysiologicalSignalProcessor
{
    Task<object> DetectAnomaliesAsync(PhysiologicalData data);
    Task<object> AnalyzeTimeSeriesAsync(PhysiologicalData data);
} 